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Linda Simoni-Wastila, PhD (lsimoniw@rx.umaryland) Christopher Blanchette, MA Xiaoqang Ren, MS PowerPoint Presentation
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Linda Simoni-Wastila, PhD (lsimoniw@rx.umaryland) Christopher Blanchette, MA Xiaoqang Ren, MS

Linda Simoni-Wastila, PhD (lsimoniw@rx.umaryland) Christopher Blanchette, MA Xiaoqang Ren, MS

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Linda Simoni-Wastila, PhD (lsimoniw@rx.umaryland) Christopher Blanchette, MA Xiaoqang Ren, MS

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  1. Gaps in Drug Benefits: Impact on Utilization and Spending for Drugs Used by Medicare Beneficiaries with Serious Mental Illness Linda Simoni-Wastila, PhD (lsimoniw@rx.umaryland.edu) Christopher Blanchette, MA Xiaoqang Ren, MS Bruce Stuart, PhD Peter Lamy Center on Drug Therapy and Aging University of Maryland Baltimore School of Pharmacy AcademyHealth Boston, MA June 28, 2005 Funded by the Robert Wood Johnson Foundation/Health Care and Financing Organization

  2. Background • There are growing concerns that the MMA Part D benefit’s “donut hole” design may result in discontinuities in access to prescribed medicines • Such coverage gaps may be particularly detrimental to older and disabled individuals with chronic conditions for whom prescription drugs represent a necessary treatment modality

  3. Background • Prior work found that drug coverage gaps reduced prescription drug use by Medicare beneficiaries. Using a simulation model, we projected total drug spending under Medicare Part D relative to those with continuous coverage: • All MC Beneficiaries: 92.1% • COPD: 79.6% • Diabetes: 83.2% • Mental Illness: 76.0% (Stuart, Simoni-Wastila and Chauncey Health Affairs web exclusive 2005)

  4. Purpose • To delve into greater detail on how drug coverage gaps impact drug use and spending by Medicare beneficiaries with serious mental illness (SMI) • Objectives: • 1) To describe extent of drug coverage gaps experienced by SMI Medicare beneficiaries; and • 2) To determine impact of coverage gaps on use of and spending for prescription drugs used to treat mental disorders

  5. Methods - Data • 1997 – 2001 Medicare Current Beneficiary Survey (MCBS) linked to Medicare Part A and Part B claims • MCBS is longitudinal, nationally-representative sample of Medicare beneficiaries • MCBS (linked to Part A and B claims) contains: • Demographics • Income and health insurance coverage, including drug benefits (with begin and end dates of coverage) • Health and functional status • Utilization and expenditures for all health services, including prescription drugs • Diagnostic information (ICD-9 diagnoses from claims; self-report from MCBS survey)

  6. Methods – Study Sample • Pooled sample of three 3-year cohorts (1997-1999, 1998-2000, and 1999-2001) of community-dwelling MCBS respondents • Excluded from analysis: M + C plan members, LTC residents, and those lost to follow-up  Sample = 9,219

  7. Methods – Study Sample • SMI defined as: 1 or more SMI diagnoses in baseline year + at least one other of same diagnosis during any of study years • SMI diagnoses include: • Schizophrenia/psychotic disorders (ICD-9 = 294.xx, 295.xx, 297.xx, 298.xx, and 299.xx) • Manic/Bipolar disorders (ICD-9 = 296.0, 296.1, 296.4-296.9) • Major depression (ICD- 9 = 296.2, 296.3) • Application of these criteria resulted in an analytic sample of 901 seriously mentally-ill Medicare beneficiaries followed for up to 3 years

  8. Methods – Dependent Variables • Mental health drug use and spending • Use defined as all Prescription Medication Events (PME) per respondent over three year period • % use, annual mean PMEs • Expenditures defined as all mental health drug spending per respondent over three period, expressed in constant 2001 dollars (and annualized) • Total mental health drug use and spending, as well as by therapeutic class: • Antipsychotics (atypicals, typicals) • Antidepressants (newer, traditional) • Anxiolytics/Sedative-hypnotics • Anti-mania drugs • Anticonvulsants (“mood-stabilizers”)

  9. Methods – Independent Variables • Prescription gap months = summed number of months over the three-year period during which the beneficiary had no evidence of prescription drug coverage • 0 Gap Months (Full drug coverage) [ref] • 1-18 Gap Months • 19-35 Gap Months • 36 Gap Months (No drug coverage)

  10. Methods - Covariates • Age (<65, 65-74, 75-84, 85+ [ref]) • Gender [Female is ref] • Race/ethnicity [Non-white is ref] • Education [<HS is ref] • Income [FPL > 300 is ref] • Non-drug supplemental insurance (0/1) • Geographic region [West is ref] • Urbanicity [Rural is ref] • Health Status [Poor is ref] • Death status (0/1 indicator of died in year 1, 2 or 3) • Psychosis or depression (0/1 indicator of condition) • Comorbidity Index (DCG/HCC)

  11. Methods – Analytic Approach • Descriptive: Mental health drug use and spending, overall and by gap status • Multivariate: Ordinary least squares regression to estimate the impact of gap status on mental health drug use and spending • Tested for endogeneity of the coverage variables and found that controlling for comorbidity (HCC/DCG) eliminated all endogeneity • All analyses weighted  nationally representative estimates

  12. Results – Baseline Characteristics

  13. Annual Mean Total and MH Drug Spending by MC Beneficiaries (unadjusted) 34.0% 10.7%

  14. Drug Coverage Gaps Among MC Beneficiaries with SMI (unadjusted)

  15. Proportion of SMI MC Beneficiaries Using Any MH Drugs, Antidepressants and Antipsychotics by Coverage Gap Status (unadjusted)

  16. Regression Results • The next several slides illustrate the impact of having coverage gaps on utilization of and spending on • All mental health drugs • Antidepressants • Antipsychotics ceteris paribus • All findings are presented as mean annual prescriptions or expenditures

  17. Annual Mean PMEs (Fills) by Coverage Gap Status(adjusted)

  18. Annual Mean Drug Spending by Coverage Gap Status (adjusted)

  19. Other Multivariate Findings • Age is important – individuals aged <65 (i.e., the disabled) had significantly increased use and spending of all MH drugs and drug classes relative to those aged 85+ • Sex, race/ethnicity, income, health status, and other covariates varied by therapeutic class • Comorbidity, as assessed using the DCG/HCC, was not a significant predictor of MH drug use or spending; however, the individual diagnoses of depression and psychotic disorders were significant positive predictors of drug use and spending

  20. Other Multivariate Findings • In within therapeutic class analyses (not shown here), we found that coverage gaps did not influence use of and spending on the newer mental health drugs, such as the atypical antipsychotics or SSRI/SNRIs, suggesting that coverage status may not influence type of drug one receives • However, when we examined the probability of receiving any “newer” MH drug (ie, any SSRI/SRNI or atypical), we found that among any antidepressant/ antipsychotic users, “newer” drug use and spending was less likely among those with gaps or no coverage relative to those with full coverage

  21. Conclusions and Next Steps • It is clear that coverage gaps make a difference in terms of access to medications used to treat Medicare beneficiaries with serious mental illness, controlling for comorbidity and other important covariates • Next Steps • Examine variation in use of and spending for other MH therapeutic categories (e.g., mood stabilizers; anxiolytics; “newer” MH drugs) • Examine how use and spending differ by age (i.e., eligibility based on disability versus age) • Answer the question: Do differences in mental health drug use due to coverage gaps impact the use of and spending on other medical services, including hospitalization, emergency department visits, and psychiatric treatment?